Ensemble of radiomics and ConvNeXt for breast cancer diagnosis

• URL: http://arxiv.org/abs/2601.05373v1
• Date: Thu, 08 Jan 2026 20:54:24 GMT
• Title: Ensemble of radiomics and ConvNeXt for breast cancer diagnosis
• Authors: Jorge Alberto Garza-Abdala, Gerardo Alejandro Fumagal-González, Beatriz A. Bosques-Palomo, Mario Alexis Monsivais Molina, Daly Avedano, Servando Cardona-Huerta, José Gerardo Tamez-Pena,
• Abstract summary: Radiomics and deep learning (DL) have shown significant potential in assisting radiologists with early cancer detection.<n>This paper aims to critically assess the performance of radiomics, DL, and ensemble techniques in detecting cancer from screening mammograms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early diagnosis of breast cancer is crucial for improving survival rates. Radiomics and deep learning (DL) have shown significant potential in assisting radiologists with early cancer detection. This paper aims to critically assess the performance of radiomics, DL, and ensemble techniques in detecting cancer from screening mammograms. Two independent datasets were used: the RSNA 2023 Breast Cancer Detection Challenge (11,913 patients) and a Mexican cohort from the TecSalud dataset (19,400 patients). The ConvNeXtV1-small DL model was trained on the RSNA dataset and validated on the TecSalud dataset, while radiomics models were developed using the TecSalud dataset and validated with a leave-one-year-out approach. The ensemble method consistently combined and calibrated predictions using the same methodology. Results showed that the ensemble approach achieved the highest area under the curve (AUC) of 0.87, compared to 0.83 for ConvNeXtV1-small and 0.80 for radiomics. In conclusion, ensemble methods combining DL and radiomics predictions significantly enhance breast cancer diagnosis from mammograms.

Related papers

• A Disease-Centric Vision-Language Foundation Model for Precision Oncology in Kidney Cancer [54.58205672910646]
  RenalCLIP is a visual-language foundation model for characterization, diagnosis and prognosis of renal mass.<n>It achieved better performance and superior generalizability across 10 core tasks spanning the full clinical workflow of kidney cancer.
  arXiv  Detail & Related papers  (2025-08-22T17:48:19Z)
• MGH Radiology Llama: A Llama 3 70B Model for Radiology [50.42811030970618]
  This paper presents an advanced radiology-focused large language model: MGH Radiology Llama.<n>It is developed using the Llama 3 70B model, building upon previous domain-specific models like Radiology-GPT and Radiology-Llama2.<n>Our evaluation, incorporating both traditional metrics and a GPT-4-based assessment, highlights the enhanced performance of this work over general-purpose LLMs.
  arXiv  Detail & Related papers  (2024-08-13T01:30:03Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Prediction of Breast Cancer Recurrence Risk Using a Multi-Model Approach Integrating Whole Slide Imaging and Clinicopathologic Features [0.6679306163028237]
  The aim of this study was to develop a multi-model approach integrating the analysis of whole slide images and clinicopathologic data to predict associated breast cancer recurrence risks. The proposed novel methodology uses convolutional neural networks for feature extraction and vision transformers for contextual aggregation.
  arXiv  Detail & Related papers  (2024-01-28T23:33:56Z)
• Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive Learning [19.948079693716075]
  Our proposed AI model utilizes supervised contrastive learning to minimize bias in CXR diagnosis. We evaluated the methods on two datasets: the Medical Imaging and Data Resource Center (MIDRC) dataset with 77,887 CXR images and the NIH Chest X-ray dataset with 112,120 CXR images.
  arXiv  Detail & Related papers  (2024-01-25T20:03:57Z)
• Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging [82.74877848011798]
  The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. The gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy. In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$s$) imaging.
  arXiv  Detail & Related papers  (2023-04-12T15:08:34Z)
• A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data [82.74877848011798]
  Cancer-Net BCa is a multi-institutional open-source benchmark dataset of volumetric CDI$s$ imaging data of breast cancer patients. Cancer-Net BCa is publicly available as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer.
  arXiv  Detail & Related papers  (2023-04-12T05:41:44Z)
• Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Synthetic Correlated Diffusion Imaging [66.63200823918429]
  We investigate enhancing clinical support for breast cancer with deep learning models. We leverage a volumetric convolutional neural network to learn deep radiomic features from a pre-treatment cohort. We find that the proposed approach can achieve better performance for both grade and post-treatment response prediction.
  arXiv  Detail & Related papers  (2022-11-10T03:02:12Z)
• Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics [13.028105771052376]
  We treat lung cancer diagnosis as a multiple instance learning (MIL) problem in order to better reflect the diagnosis process in the clinical setting. We chose radiomics as the source of input features and deep attention-based MIL as the classification algorithm. The results show that our method can achieve a mean accuracy of 0.807 with a standard error of the mean (SEM) of 0.069, a recall of 0.870 (SEM 0.061), a positive predictive value of 0.928 (SEM 0.078), a negative predictive value of 0.591 (SEM 0.155) and an area under the curve (AUC) of 0.842 (
  arXiv  Detail & Related papers  (2021-04-29T21:04:02Z)
• Automatic Generation of Interpretable Lung Cancer Scoring Models from Chest X-Ray Images [9.525711971667679]
  Lung cancer is the leading cause of cancer death worldwide. Deep learning techniques are effective at automatically diagnosing lung cancer. These techniques have yet to be clinically approved and adopted by the medical community.
  arXiv  Detail & Related papers  (2020-12-10T04:11:59Z)
• Predicting the risk of pancreatic cancer with a CT-based ensemble AI algorithm [0.0]
  Pancreatic cancer is a lethal disease, hard to diagnose and results in poor prognosis and high mortality. We propose an ensemble AI algorithm to predict universally cancer risk of all kinds of pancreatic lesions with noncontrast CT.
  arXiv  Detail & Related papers  (2020-04-03T06:06:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.